Medical documents and medical images are becoming digital along with recent popularization of medical information systems including HIS and PACS. HIS stands for a hospital information system, and PACS stands for a picture archiving and communication system.
Medical images (e.g., X-ray image, CT image, and MRI image), which were often viewed on a film viewer after developed on films, are digitized now and can be viewed on a monitor.
More specifically, digital medical images (medical image data) can be stored in the PACS or the like, and if necessary, read out from it and interpreted on the monitor of an image interpretation terminal.
Medical documents such as a medical record are also being digitized as medical record data. Medical record data of a patient serving as an object can be read out from the HIS or the like and viewed on the monitor of an image interpretation terminal.
In the digital environment, an image interpreter can receive an image interpretation request form by a digital message. Based on the message, he reads out medical image data of a patient from the PACS and makes a diagnosis while displaying it on the image interpretation monitor of an image interpretation terminal. If necessary, the image interpreter reads out medical record data of the patient from the HIS and makes a diagnosis while displaying it on another monitor.
A desire to reduce the burden on an image interpreter in image interpretation has urged the development of medical image processing apparatuses. This apparatus makes a computer-aided diagnosis by analyzing medical image data to automatically detect a morbid portion or the like. Computer-aided diagnosis will be referred to as CAD.
CAD can automatically detect an abnormal shadow candidate as a morbid portion and display it. More specifically, a computer can process medical image data such as an X-ray image to detect and display an abnormal tumor shadow or high-density small calcified shadow caused by a cancer or the like. The use of CAD can reduce the burden on an image interpreter in image interpretation and increase the image interpretation accuracy.
Another technique for reducing the burden on an image interpreter in image interpretation is disclosed in, for example, patent reference 1 listed below. The technique in patent reference 1 can automatically detect an abnormal candidate from medical image data and automatically set a region (to be referred to as a region of interest) containing the abnormal candidate portion. This technique can save an image interpreter from having to manually set a region of interest.
Demand has also arisen for developing a technique for further increasing the image interpretation accuracy by an image interpreter in image interpretation. Generally when interpreting medical image data to make a diagnosis, an image interpreter sometimes hesitates to decide a diagnosis name if a morbid portion in the medical image data during interpretation has an unfamiliar image feature or there are a plurality of morbid portions having similar image features.
In this case, the image interpreter at a loss may ask advice for another experienced image interpreter, or refer to documents such as medical books and read the description of an image feature regarding a suspicious disease name. Alternatively, he may examine illustrated medical documents to locate a photo similar to a morbid portion captured in the medical image data during image interpretation, and read a disease name corresponding to the photo for reference of the diagnosis.
However, the image interpreter may not always have an advisory experienced image interpreter. Even if the image interpreter examines medical documents, he may not be able to locate a photo similar to a morbid portion captured in the medical image data during image interpretation, or the description of an image feature.
To solve this problem by a digital means and increase the image interpretation accuracy, similar case search apparatuses have been developed recently. The basic idea of the similar case search apparatus is to support a diagnosis by searching for a plurality of case data from those accumulated in the past based on any criterion and presenting them to an image interpreter.
As a general method in similar case search, it is known to search an image database accumulated in the past for image data similar in image feature amount to medical image data during image interpretation.
Diagnosis is sometimes made based on the result of follow-up such as the progress of a disease. In this case, the similarity of medical image data at one time point is determined. Also, the similarity of the process is determined based on a plurality of medical image data obtained by imaging the same patient in different periods. By presenting case data similar in process, this method can present highly reliable reference information in disease diagnosing, subsequent inspection planning, treatment planning, and the like.
For example, patent reference 2 listed below discloses a similar case search method which assists a diagnosis by determining the similarity of the process based on a plurality of medical image data obtained by imaging the same patient in different periods.
According to patent reference 2, case data which are similar in image feature amount to respective time-series medical image data to be inspected and are equal in imaging time interval to them can be presented as case data similar in the process of a disease.